Aiming at the problem that Kernel-based Fuzzy C-Means (KFCM) algorithm is sensitive to the initial clustering center and is easy to fall into the local optimum, and the fact that Artificial Bee Colony (ABC) algorithm is simple and of high global convergence speed, a new clustering algorithm based on Improved Artificial Bee Colony (IABC) algorithm and KFCM iteration was proposed. Firstly, the optimal solution was obtained by using IABC as the initial clustering center of the KFCM algorithm. IABC algorithm improved the search behavior of the employed bee with the change rate of the difference from the current dimension optimal solution in the iterative process, balancing the global search and local mining ability of the artificial bee colony algorithm. Secondly, based on within-class distance and between-class distance, the fitness function of the KFCM algorithm was constructed and the cluster center was optimized by KFCM algorithm. Finally, the IABC and KFCM algorithms were executed alternately to achieve optimal clustering. Three Benchmark test functions and six sets in UCI standard data set was used to carry out simulation experiments. The experimental results show that IABC-KFCM improves the clustering effectiveness index of data set by 1 to 4 percentage points compared to IABC-GFCM (Generalized Fuzzy Clustering algorithm based on Improved ABC), which has the advantages of strong robustness and high clustering precision.

The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.